Today, LRI is a proven manufacturing technology for both small and large scale structures (e.g. sailboats) where, in most cases, experience and limited prototype experimentation is sufficient to get a satisfactory design. However, large scale aerospace (and other) structures require reproducible, high quality, defect free parts, with excellent mechanical performance. This requires precise control and knowledge of the preforming (draping and manufacture of the composite fabric preforms), their assembly and the resin infusion. The INFUCOMP project is a multi-disciplinary research project to develop necessary Computer Aided Engineering (CAE) tools for all stages of the LRI manufacturing process. An ambitious set of developments have been undertaken that build on existing capabilities of leading drape and infusion simulation codes available today.Currently the codes are only accurate for simple drape problems and infusion analysis of RTM parts using matched metal molds. Furthermore, full chaining of the CAE solution will allow results from materials modeling, drape, assembly, infusion and final part mechanical performance to be used in subsequent analyses.Although the materials and manufacturing methods in INFUCOMP are specific to aerospace structures, it is expected that the work would be of great value to other industries, including energy (windmill), rail, sea, advanced automotive and manufacturing.INFUCOMP has built on PAM-RTM, an existing simulation software, to provide a full solution chain for LRI composites; including fabric modeling, drape, assembly, infusion, cost and final part performance prediction. Simulation tools will avoid costly and time consuming prototype testing, will allow the CAE design of alternative manufacturing routes and enable cost effective, efficient LRI composite structures to be designed and manufactured.This paper presents the work carried out during industrial validation phase of the project on simplified industrial components and an industrially relevant LRI aircraft sub-structure.This work has used several specific developments including numerous enhancements to the state-of-the-art for resin infusion simulation; in particular, better viscosity models and essential developments to run under DMP (Distributed Memory Processing) to take advantage of new generation cluster computers and massive parallel computing. Some details about coupling of modeling and monitoring allowing a combination of predictive capabilities provided by simulation with the capability of detecting unexpected events and variations in real time provided by process monitoring will be presented.